35
for this service is given by allocation. In prospective modeling of society’s future
metabolism at full scale, however, the tight coupling between different material
cycles ultimately necessitates parallel modeling of different materials across products and over time. Only then can one assess whether and how system-wide supply
can meet system-wide demand for different chemical elements at different stages of
the material cycles. Supply-demand imbalances may arise under business-as-usual
assumptions, as studies for aluminum (Modaresi and Müller 2012 ) and rare earth
metals (Elshkaki and Graedel 2014 ) show, which points out the necessity to design
future material cycles from a systems’ perspective.
For dynamic MFA , several trends that point toward comprehensive assessment
of multi-material product portfolios are already emerging. One trend goes toward a
higher level of detail of material types (alloys) and products studied to better understand quality issues in the recycling systems of different materials (Løvik et al.
2014 ; Ohno et al. 2014 ). Another trend goes toward modeling of co-occurrence,
co-mining, and co-production of mineral and metal resources and production
systems and energy-ore grade relationships (Graedel et al. 2013 ; Northey et al.
2014 ). Finally, there are recent advances in the modeling of the fate of the end-oflife materials from the waste management industries back into new products using
Markov chains and supply-driven I/O modeling (Duchin and Levine 2013 ;
Nakamura et al. 2014 ).
The trend of using I/O models for prospective assessments is also likely to continue. Service-driven modeling, or – if stocks are used as proxy for services – stockdriven modeling, can be used (a) to determine the fi nal demand vector for I/O models
(Kagawa et al. 2015 ) and (b) to determine the Leontief-A matrix from an age-cohortbased model of the productive capital stock (Pauliuk et al. 2015 ). Multilayer modeling (Schmidt et al. 2012 ) can be used to cover different materials in a common I/O
framework, and when building I/O models of future industrial systems, the by-product technology construct can be applied to avoid allocation (Majeau-Bettez et al.
2014 ). A major application of the so-obtained I/O models is the prospective attributional assessment of certain quanta of fi nal demand to measure strategy performance
and derive policy targets related to specifi c transformation strategies.
4.2 Linking Industrial Ecology and Integrated Assessment
Models (IAMs)
The most prominent contribution to prospective modeling of society’s metabolism
did not emerge from industrial ecology but from other disciplines, especially integrated assessment modeling. While widely successful in generating integrated scenarios of the society’ future metabolism, the biosphere, and the climate system,
integrated assessment models were criticized for several shortcomings (Arvesen
et al. 2011 ; Pindyck 2013 ; Stern 2013 ). To our knowledge, a detailed criticism of
IAMs from an industrial ecology perspective is still lacking; it is also beyond the
scope of this chapter; however, below we list central points of critique and propose
some ideas for the integration of IE principles into IAMs.
2 Prospective Models of Society’s Future Metabolism: What Industrial Ecology Has…
for this service is given by allocation. In prospective modeling of society’s future
metabolism at full scale, however, the tight coupling between different material
cycles ultimately necessitates parallel modeling of different materials across products and over time. Only then can one assess whether and how system-wide supply
can meet system-wide demand for different chemical elements at different stages of
the material cycles. Supply-demand imbalances may arise under business-as-usual
assumptions, as studies for aluminum (Modaresi and Müller 2012 ) and rare earth
metals (Elshkaki and Graedel 2014 ) show, which points out the necessity to design
future material cycles from a systems’ perspective.
For dynamic MFA , several trends that point toward comprehensive assessment
of multi-material product portfolios are already emerging. One trend goes toward a
higher level of detail of material types (alloys) and products studied to better understand quality issues in the recycling systems of different materials (Løvik et al.
2014 ; Ohno et al. 2014 ). Another trend goes toward modeling of co-occurrence,
co-mining, and co-production of mineral and metal resources and production
systems and energy-ore grade relationships (Graedel et al. 2013 ; Northey et al.
2014 ). Finally, there are recent advances in the modeling of the fate of the end-oflife materials from the waste management industries back into new products using
Markov chains and supply-driven I/O modeling (Duchin and Levine 2013 ;
Nakamura et al. 2014 ).
The trend of using I/O models for prospective assessments is also likely to continue. Service-driven modeling, or – if stocks are used as proxy for services – stockdriven modeling, can be used (a) to determine the fi nal demand vector for I/O models
(Kagawa et al. 2015 ) and (b) to determine the Leontief-A matrix from an age-cohortbased model of the productive capital stock (Pauliuk et al. 2015 ). Multilayer modeling (Schmidt et al. 2012 ) can be used to cover different materials in a common I/O
framework, and when building I/O models of future industrial systems, the by-product technology construct can be applied to avoid allocation (Majeau-Bettez et al.
2014 ). A major application of the so-obtained I/O models is the prospective attributional assessment of certain quanta of fi nal demand to measure strategy performance
and derive policy targets related to specifi c transformation strategies.
4.2 Linking Industrial Ecology and Integrated Assessment
Models (IAMs)
The most prominent contribution to prospective modeling of society’s metabolism
did not emerge from industrial ecology but from other disciplines, especially integrated assessment modeling. While widely successful in generating integrated scenarios of the society’ future metabolism, the biosphere, and the climate system,
integrated assessment models were criticized for several shortcomings (Arvesen
et al. 2011 ; Pindyck 2013 ; Stern 2013 ). To our knowledge, a detailed criticism of
IAMs from an industrial ecology perspective is still lacking; it is also beyond the
scope of this chapter; however, below we list central points of critique and propose
some ideas for the integration of IE principles into IAMs.
2 Prospective Models of Society’s Future Metabolism: What Industrial Ecology Has…